{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import numpy as np #矩阵操作\n",
    "import pandas as pd #SQL数据处理\n",
    "\n",
    "from sklearn.metrics import r2_score #评价回归预测模型的性能\n",
    "\n",
    "import matplotlib.pyplot as plt #画图\n",
    "import seaborn as sns\n",
    "\n",
    "#图形出现Notebook内联\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.数据探索"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.1数据导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# path to where the data lies\n",
    "dpath = './Bike-Sharing-Dataset/'\n",
    "data = pd.read_csv(dpath+\"day.csv\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(731, 16)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape #数据规模"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of      instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0          1  2011-01-01       1   0     1        0        6           0   \n",
       "1          2  2011-01-02       1   0     1        0        0           0   \n",
       "2          3  2011-01-03       1   0     1        0        1           1   \n",
       "3          4  2011-01-04       1   0     1        0        2           1   \n",
       "4          5  2011-01-05       1   0     1        0        3           1   \n",
       "5          6  2011-01-06       1   0     1        0        4           1   \n",
       "6          7  2011-01-07       1   0     1        0        5           1   \n",
       "7          8  2011-01-08       1   0     1        0        6           0   \n",
       "8          9  2011-01-09       1   0     1        0        0           0   \n",
       "9         10  2011-01-10       1   0     1        0        1           1   \n",
       "10        11  2011-01-11       1   0     1        0        2           1   \n",
       "11        12  2011-01-12       1   0     1        0        3           1   \n",
       "12        13  2011-01-13       1   0     1        0        4           1   \n",
       "13        14  2011-01-14       1   0     1        0        5           1   \n",
       "14        15  2011-01-15       1   0     1        0        6           0   \n",
       "15        16  2011-01-16       1   0     1        0        0           0   \n",
       "16        17  2011-01-17       1   0     1        1        1           0   \n",
       "17        18  2011-01-18       1   0     1        0        2           1   \n",
       "18        19  2011-01-19       1   0     1        0        3           1   \n",
       "19        20  2011-01-20       1   0     1        0        4           1   \n",
       "20        21  2011-01-21       1   0     1        0        5           1   \n",
       "21        22  2011-01-22       1   0     1        0        6           0   \n",
       "22        23  2011-01-23       1   0     1        0        0           0   \n",
       "23        24  2011-01-24       1   0     1        0        1           1   \n",
       "24        25  2011-01-25       1   0     1        0        2           1   \n",
       "25        26  2011-01-26       1   0     1        0        3           1   \n",
       "26        27  2011-01-27       1   0     1        0        4           1   \n",
       "27        28  2011-01-28       1   0     1        0        5           1   \n",
       "28        29  2011-01-29       1   0     1        0        6           0   \n",
       "29        30  2011-01-30       1   0     1        0        0           0   \n",
       "..       ...         ...     ...  ..   ...      ...      ...         ...   \n",
       "701      702  2012-12-02       4   1    12        0        0           0   \n",
       "702      703  2012-12-03       4   1    12        0        1           1   \n",
       "703      704  2012-12-04       4   1    12        0        2           1   \n",
       "704      705  2012-12-05       4   1    12        0        3           1   \n",
       "705      706  2012-12-06       4   1    12        0        4           1   \n",
       "706      707  2012-12-07       4   1    12        0        5           1   \n",
       "707      708  2012-12-08       4   1    12        0        6           0   \n",
       "708      709  2012-12-09       4   1    12        0        0           0   \n",
       "709      710  2012-12-10       4   1    12        0        1           1   \n",
       "710      711  2012-12-11       4   1    12        0        2           1   \n",
       "711      712  2012-12-12       4   1    12        0        3           1   \n",
       "712      713  2012-12-13       4   1    12        0        4           1   \n",
       "713      714  2012-12-14       4   1    12        0        5           1   \n",
       "714      715  2012-12-15       4   1    12        0        6           0   \n",
       "715      716  2012-12-16       4   1    12        0        0           0   \n",
       "716      717  2012-12-17       4   1    12        0        1           1   \n",
       "717      718  2012-12-18       4   1    12        0        2           1   \n",
       "718      719  2012-12-19       4   1    12        0        3           1   \n",
       "719      720  2012-12-20       4   1    12        0        4           1   \n",
       "720      721  2012-12-21       1   1    12        0        5           1   \n",
       "721      722  2012-12-22       1   1    12        0        6           0   \n",
       "722      723  2012-12-23       1   1    12        0        0           0   \n",
       "723      724  2012-12-24       1   1    12        0        1           1   \n",
       "724      725  2012-12-25       1   1    12        1        2           0   \n",
       "725      726  2012-12-26       1   1    12        0        3           1   \n",
       "726      727  2012-12-27       1   1    12        0        4           1   \n",
       "727      728  2012-12-28       1   1    12        0        5           1   \n",
       "728      729  2012-12-29       1   1    12        0        6           0   \n",
       "729      730  2012-12-30       1   1    12        0        0           0   \n",
       "730      731  2012-12-31       1   1    12        0        1           1   \n",
       "\n",
       "     weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0             2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1             2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2             1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3             1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4             1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "5             1  0.204348  0.233209  0.518261   0.089565      88        1518   \n",
       "6             2  0.196522  0.208839  0.498696   0.168726     148        1362   \n",
       "7             2  0.165000  0.162254  0.535833   0.266804      68         891   \n",
       "8             1  0.138333  0.116175  0.434167   0.361950      54         768   \n",
       "9             1  0.150833  0.150888  0.482917   0.223267      41        1280   \n",
       "10            2  0.169091  0.191464  0.686364   0.122132      43        1220   \n",
       "11            1  0.172727  0.160473  0.599545   0.304627      25        1137   \n",
       "12            1  0.165000  0.150883  0.470417   0.301000      38        1368   \n",
       "13            1  0.160870  0.188413  0.537826   0.126548      54        1367   \n",
       "14            2  0.233333  0.248112  0.498750   0.157963     222        1026   \n",
       "15            1  0.231667  0.234217  0.483750   0.188433     251         953   \n",
       "16            2  0.175833  0.176771  0.537500   0.194017     117         883   \n",
       "17            2  0.216667  0.232333  0.861667   0.146775       9         674   \n",
       "18            2  0.292174  0.298422  0.741739   0.208317      78        1572   \n",
       "19            2  0.261667  0.255050  0.538333   0.195904      83        1844   \n",
       "20            1  0.177500  0.157833  0.457083   0.353242      75        1468   \n",
       "21            1  0.059130  0.079070  0.400000   0.171970      93         888   \n",
       "22            1  0.096522  0.098839  0.436522   0.246600     150         836   \n",
       "23            1  0.097391  0.117930  0.491739   0.158330      86        1330   \n",
       "24            2  0.223478  0.234526  0.616957   0.129796     186        1799   \n",
       "25            3  0.217500  0.203600  0.862500   0.293850      34         472   \n",
       "26            1  0.195000  0.219700  0.687500   0.113837      15         416   \n",
       "27            2  0.203478  0.223317  0.793043   0.123300      38        1129   \n",
       "28            1  0.196522  0.212126  0.651739   0.145365     123         975   \n",
       "29            1  0.216522  0.250322  0.722174   0.073983     140         956   \n",
       "..          ...       ...       ...       ...        ...     ...         ...   \n",
       "701           2  0.347500  0.359208  0.823333   0.124379     892        3757   \n",
       "702           1  0.452500  0.455796  0.767500   0.082721     555        5679   \n",
       "703           1  0.475833  0.469054  0.733750   0.174129     551        6055   \n",
       "704           1  0.438333  0.428012  0.485000   0.324021     331        5398   \n",
       "705           1  0.255833  0.258204  0.508750   0.174754     340        5035   \n",
       "706           2  0.320833  0.321958  0.764167   0.130600     349        4659   \n",
       "707           2  0.381667  0.389508  0.911250   0.101379    1153        4429   \n",
       "708           2  0.384167  0.390146  0.905417   0.157975     441        2787   \n",
       "709           2  0.435833  0.435575  0.925000   0.190308     329        4841   \n",
       "710           2  0.353333  0.338363  0.596667   0.296037     282        5219   \n",
       "711           2  0.297500  0.297338  0.538333   0.162937     310        5009   \n",
       "712           1  0.295833  0.294188  0.485833   0.174129     425        5107   \n",
       "713           1  0.281667  0.294192  0.642917   0.131229     429        5182   \n",
       "714           1  0.324167  0.338383  0.650417   0.106350     767        4280   \n",
       "715           2  0.362500  0.369938  0.838750   0.100742     538        3248   \n",
       "716           2  0.393333  0.401500  0.907083   0.098258     212        4373   \n",
       "717           1  0.410833  0.409708  0.666250   0.221404     433        5124   \n",
       "718           1  0.332500  0.342162  0.625417   0.184092     333        4934   \n",
       "719           2  0.330000  0.335217  0.667917   0.132463     314        3814   \n",
       "720           2  0.326667  0.301767  0.556667   0.374383     221        3402   \n",
       "721           1  0.265833  0.236113  0.441250   0.407346     205        1544   \n",
       "722           1  0.245833  0.259471  0.515417   0.133083     408        1379   \n",
       "723           2  0.231304  0.258900  0.791304   0.077230     174         746   \n",
       "724           2  0.291304  0.294465  0.734783   0.168726     440         573   \n",
       "725           3  0.243333  0.220333  0.823333   0.316546       9         432   \n",
       "726           2  0.254167  0.226642  0.652917   0.350133     247        1867   \n",
       "727           2  0.253333  0.255046  0.590000   0.155471     644        2451   \n",
       "728           2  0.253333  0.242400  0.752917   0.124383     159        1182   \n",
       "729           1  0.255833  0.231700  0.483333   0.350754     364        1432   \n",
       "730           2  0.215833  0.223487  0.577500   0.154846     439        2290   \n",
       "\n",
       "      cnt  \n",
       "0     985  \n",
       "1     801  \n",
       "2    1349  \n",
       "3    1562  \n",
       "4    1600  \n",
       "5    1606  \n",
       "6    1510  \n",
       "7     959  \n",
       "8     822  \n",
       "9    1321  \n",
       "10   1263  \n",
       "11   1162  \n",
       "12   1406  \n",
       "13   1421  \n",
       "14   1248  \n",
       "15   1204  \n",
       "16   1000  \n",
       "17    683  \n",
       "18   1650  \n",
       "19   1927  \n",
       "20   1543  \n",
       "21    981  \n",
       "22    986  \n",
       "23   1416  \n",
       "24   1985  \n",
       "25    506  \n",
       "26    431  \n",
       "27   1167  \n",
       "28   1098  \n",
       "29   1096  \n",
       "..    ...  \n",
       "701  4649  \n",
       "702  6234  \n",
       "703  6606  \n",
       "704  5729  \n",
       "705  5375  \n",
       "706  5008  \n",
       "707  5582  \n",
       "708  3228  \n",
       "709  5170  \n",
       "710  5501  \n",
       "711  5319  \n",
       "712  5532  \n",
       "713  5611  \n",
       "714  5047  \n",
       "715  3786  \n",
       "716  4585  \n",
       "717  5557  \n",
       "718  5267  \n",
       "719  4128  \n",
       "720  3623  \n",
       "721  1749  \n",
       "722  1787  \n",
       "723   920  \n",
       "724  1013  \n",
       "725   441  \n",
       "726  2114  \n",
       "727  3095  \n",
       "728  1341  \n",
       "729  1796  \n",
       "730  2729  \n",
       "\n",
       "[731 rows x 16 columns]>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.info #数据信息"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2数据分割"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data_2011 = data[data.yr==0] #2011年数据作为训练集\n",
    "data_2012 = data[data.yr==1] #2012年数据作为测试集"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.3数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.0</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "      <td>365.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>2.498630</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.526027</td>\n",
       "      <td>0.027397</td>\n",
       "      <td>3.008219</td>\n",
       "      <td>0.684932</td>\n",
       "      <td>1.421918</td>\n",
       "      <td>0.486665</td>\n",
       "      <td>0.466835</td>\n",
       "      <td>0.643665</td>\n",
       "      <td>0.191403</td>\n",
       "      <td>677.402740</td>\n",
       "      <td>2728.358904</td>\n",
       "      <td>3405.761644</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>105.510663</td>\n",
       "      <td>1.110946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.452584</td>\n",
       "      <td>0.163462</td>\n",
       "      <td>2.006155</td>\n",
       "      <td>0.465181</td>\n",
       "      <td>0.571831</td>\n",
       "      <td>0.189596</td>\n",
       "      <td>0.168836</td>\n",
       "      <td>0.148744</td>\n",
       "      <td>0.076890</td>\n",
       "      <td>556.269121</td>\n",
       "      <td>1060.110413</td>\n",
       "      <td>1378.753666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>431.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>92.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.325000</td>\n",
       "      <td>0.321954</td>\n",
       "      <td>0.538333</td>\n",
       "      <td>0.135583</td>\n",
       "      <td>222.000000</td>\n",
       "      <td>1730.000000</td>\n",
       "      <td>2132.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>183.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479167</td>\n",
       "      <td>0.472846</td>\n",
       "      <td>0.647500</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>614.000000</td>\n",
       "      <td>2915.000000</td>\n",
       "      <td>3740.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>274.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.656667</td>\n",
       "      <td>0.612379</td>\n",
       "      <td>0.742083</td>\n",
       "      <td>0.235075</td>\n",
       "      <td>871.000000</td>\n",
       "      <td>3632.000000</td>\n",
       "      <td>4586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>365.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.849167</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3065.000000</td>\n",
       "      <td>4614.000000</td>\n",
       "      <td>6043.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season     yr        mnth     holiday     weekday  \\\n",
       "count  365.000000  365.000000  365.0  365.000000  365.000000  365.000000   \n",
       "mean   183.000000    2.498630    0.0    6.526027    0.027397    3.008219   \n",
       "std    105.510663    1.110946    0.0    3.452584    0.163462    2.006155   \n",
       "min      1.000000    1.000000    0.0    1.000000    0.000000    0.000000   \n",
       "25%     92.000000    2.000000    0.0    4.000000    0.000000    1.000000   \n",
       "50%    183.000000    3.000000    0.0    7.000000    0.000000    3.000000   \n",
       "75%    274.000000    3.000000    0.0   10.000000    0.000000    5.000000   \n",
       "max    365.000000    4.000000    0.0   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  365.000000  365.000000  365.000000  365.000000  365.000000  365.000000   \n",
       "mean     0.684932    1.421918    0.486665    0.466835    0.643665    0.191403   \n",
       "std      0.465181    0.571831    0.189596    0.168836    0.148744    0.076890   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.325000    0.321954    0.538333    0.135583   \n",
       "50%      1.000000    1.000000    0.479167    0.472846    0.647500    0.186900   \n",
       "75%      1.000000    2.000000    0.656667    0.612379    0.742083    0.235075   \n",
       "max      1.000000    3.000000    0.849167    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   365.000000   365.000000   365.000000  \n",
       "mean    677.402740  2728.358904  3405.761644  \n",
       "std     556.269121  1060.110413  1378.753666  \n",
       "min       9.000000   416.000000   431.000000  \n",
       "25%     222.000000  1730.000000  2132.000000  \n",
       "50%     614.000000  2915.000000  3740.000000  \n",
       "75%     871.000000  3632.000000  4586.000000  \n",
       "max    3065.000000  4614.000000  6043.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_2011.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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89BRpgjmimANUOOf2OOfagKXA4tPWWQw87j1+Fphv3RPnLwaWOudanXN7gQqvvT7bdM6t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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d485e3990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 目标y（cnt）的直方图／分布\n",
    "fig = plt.figure()\n",
    "sns.distplot(data_2011.cnt.values, bins=30, kde=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f7d44cbed10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AIRFMgsC0SMsfwuhPgWAqwKIjqERqjqO+VmdiMZmK+tbXUV7REDaqU0K2NMf5tflQ05cX\n9jp853CNEqinyH0U5ag7n3pTSPsHCo/f/njNgrFzxXMAspVltR0QBSAkgq3mrg4vP31N+J9h5OrV\njvWKmgSdsGHHMdmNg0JNF07Z1dsFXmFrVE7w/E0zojDTlovpa2lhCVv2b8HAjQPGWU6Y78C2glg3\nYg+GhnrnE7Wou6ksZJCo6bgFN0QBCIlgc+Jp0z5YqlDpWFlewSW/cglG/kz/8Lscx0VIz03MYePY\nxionZyXVwX89ElpVy9Yff74hHa7hl9532RzeXrsuCqertwulsyWMd42HClTb+oTlt5bRk+/RRulE\nTSFt2zaOcFtZZFZGFICQCLoRZ3DU7uEXEJEEgiVaxuU41EVVK1BNQnr+6Lx2dDy0bQjTO6a1ppeg\nwDOZXyKvSg4QxfzhX/sQ9Ct4SiEsrXSYYi0uFjF6aNTZZBQlAisuO3+jqbTbCYkCEhIhGO2R78+D\niGqEf251rsqhGPUhN0XLuByHz3NVhEo9dXy37N+C0UOj1qiWwmTB6MvwtrcVcPfT1dtVjvqxRNCE\nMbRtqBK1k1udq0nOZotAClOsXvoO7/g7X9hZVZs4GB00uHmwJtrHGyj4idPOH7bIrJOQGYAQO8GR\n3uihURy7+1iNoAGA0r9UpwbW2qpDMnjqhJJrvLzfF1BvQrcwk8TMrhljplC/6cEacRSShqJeoio9\n24jdJqRtsytd0RlvnyRMNPUo+nZFFIAQK6aoDmMmSAYOjx3G1G1TlQfdVIXKmNtHI6BtNu8g3oOf\nVISJUbAwqkbHJupJ8exKVKVnUqxhK6FtQtekQJNKDhd35tZWRhSAECuuUR1+gsVObj5ws1bgRRXQ\nQ9uGMLNrJlQB+J2o3jk0KmTC6hgDF9Ys2OzqrgrIVTi6RDmFXVOvz/6iL8XFYsWEog2nbVDoxmm3\nl1DSC4gCEGKl0Wm0bVl/PQI6rD/BBz+uCBOXOsals6XyQrcIqTFciq+YhGOUKCfbNfA7kxtJYRFF\n6H7rzm/FlgIiTkXf6kgyOCFW4kpWVk8Vraj9cSn1GOt3anwZvRf3oiffY5yleH0E9OYs2/5B01FY\nLd6oRD1evSac6R3T2uymAGK9V9oJSQYnpIIp/FNXCtCEySxQjwCJmsogDowKUDPWMsXO+481desU\nunq6sHJOv0DKtT5wnM5PW4oO0/HqnV2dOHDC+Fkn2u3jRMJAhVjRJfu65au3YOvBrVVtxgRmKjIm\niCmM0AvhjNKfJIW/LeTTRHGxGBoKqhP+YQSFo0lYRhWioU7rmIWybcV1J9rt40RmAEJs6MI/g6mZ\n/dvqwj2H7xyuO8mYiTgLtYRhC/m0pUnw+lhvbYJg8RWdfT0u52ccTusoUBdpayAHU3+bMPlNxAcg\nCkCIAV3B8LAoDVPFq/mj89r6sK0Su20L+dz0wKZQARy1NkHvxb3Y9MAmAOECLS7nZ9R6Do1QmCyA\nDRox945czb0SFuWkSzbXySuBRQEIDRGWG8Y2QvfaXSJJWiV229jP9fYC9x4je0YwdduU0ywg35/H\nNZ+8pmbW5X2Hf21FWNbTuM8xLmZ2zQAG61fpbKmmfGXwXtKlybYlmxMFIAgRCMsNEzZCN5l2jt19\nrKF49bQIM7OECeChbUNYeGqhRnB19XZh1aWrUFwsWsM/mzG6DTvHOBOthd0/fsGtvRcjmNOyNpts\nBqIAhIZwyQ1Tz/7FM8WqBGW2ePWsZXbUFTKJ0p+w1M4e+zbsi5RKOa5rElYjIM5Eay4mMdfylS7f\n1WmIAhAaIuwBXTq1hH0b9hmFsqvN25SVM0uZHXXmMK+QSVRcTDX1pFKOC1P/GnHW63DJ6eQJbpOT\n3YUsziabgYSBCg2hq90axBay6bJ/5TgaIZaVzI6FyQIOjx1ual+ijFibNbqN21lfFcYL1ITYeoK7\nMFnQlhp1Id+fTzQ0OMs4KwAi6iaifyCib6n37yaip4lonoi+QUQ51b5KvT+pPt/gO8Z9qv2nRPTR\nuE9GaD5D24awcWxjaOy7SRB6D7gLOiEW9+ImXRF7l/2ObD/S9ILlOuWZdCrlMOJaa+Cnkl6adxtT\nb8/smgnNOWUitzrXkcIfiDYDuBvA8773fwzgy8w8COB1AHeo9jsAvM7M7wXwZbUdiOh9AD4F4BoA\nNwHYT0TVd6rQkswfnXdyttlWiIblwzcJMZNgISJnAQ7Uv9AMCHeEJzX6dl1018zRrU4pxamAdLUG\ngMaUbCc6fz2cfABEdBWALQD2APg9IiIAHwbwn9UmEwD+AMCDALaq1wDwTQB/orbfCuBRZn4bwM+J\n6CSAGwB8L5YzEVLBlhIgiE0Q2my9NkfqyJ4RbbppXmE88ZknALj5AhqxXdsESNKjb9dUys0irURr\nUddPBPeNQtaCDhrBdQawD8A9uBCR2w/gDWb2PFynAVypXl8J4EUAUJ8vqe0r7Zp9KhDRdiKaJaLZ\nV199NcKpCM0mLCWAnzBB6I1mdSkibI7UoW1DWHXpKu1n50vnMbNrxsm004gpyTgL6aaOtC0PbRu6\nUO5xYanyGyTJyJ6RyCk4gOgKupGZYhYJVQBE9LsAXmFmf0Ym3aXmkM9s+1xoYD7AzMPMPLxu3bqw\n7gkpEmb6oK7yT+5qhhjaNoTc6lxNe5gjtbhojvzwHtCwB7YR27XJ7PGxiY91nPAHmiMkg0odgJMZ\nstGSmlkJOogLFxPQjQD+IxFtBnARgEtRnhGsIaIeNcq/CsBLavvTAK4GcJqIegD0AVj0tXv49xFa\nENvoePizw5WY/TiOuXRqSZsiArBP/6mbnEw7jeTJyWp++bRMFXGHggYxhf7m++1hoNRNuOWrt0Tu\ng/86mpRMq/oRQhUAM98H4D4AIKIPAfh9Zt5GRH8F4OMAHgUwBuAJtcuT6v331OffZmYmoicB/AUR\nfQnArwAYBPCDeE9HaCYmwZvvz9fkX3GNzbcJc1uxEZ0foDvXbUxBHXxgGxXi9aZY8AsXf3WtRgV2\nmusjks7bZFIwtrTaQNkvVI/wd6kt3aqLyBpZB/B5lB3CJ1G28T+s2h8G0K/afw/AvQDAzM8BeAzA\nTwD8DYC7mNktQbyQSUymDwB1T5Nt6wJsoaS3fPWWKv9Bvj9/IRpGg6mOsC7CJCmCppLK6ueIZhO/\nOWTv5Xux9/K9mLp1KjVTRRKhoH6Mq8dVWm3q1jsD6vn+MDOnh1fdrdWItBKYmb8L4Lvq9c9QjuIJ\nbvOvAD5h2H8PypFEQhtgGjVP3Tal3d5lBOgdc+rWaMewjcBNpp20TCSV7w2JWgmaTVzKQYathG2G\nqSLpmru2xIC6BIONfL/r9SqeKbZkRlFJBSFEJkxwmoSb6wjMW9gTR/ZPk5IC3LKQxo2rScHDE0Am\nk44/75ALzTBVJO0T0YYMEzC4eTD27zcpGy99uR9XhZ0lX5HUBBYioRNgwRKLLtvE8T2NEHd93Ea/\n14TXnzhqLSddCrOZTO+YrsmY6np+UWZ+psJFxogjVaPYpOiDpT2T+k1cawJLLiAhEi5hcHGUYUy6\nlGNaBWaiHN9vtmg402WTVwQnjW71uYuPI2qIqjYXkWXM7M2wTL6DYGnPtENIxQQkRMJVcMZReCTJ\nUo5pFZixRTl5TmxdFJAt4ipYDtJPnCPMLK2AtYUL27LPutaf0BXRCZuF1auw0wwhFQUgRKJVKnOF\nkbSjMur3hglp037BcpBxhpL6MfkgFp5a0NZoaOR7XJSMTZHa+uZaf0LnD7IJ6r71bgpbu2+Kz474\nAIRIJG2bbyZZiALynIlBAZKl/gIW30XAJNLIvRDl3nJypmv61pPvca4ZEPQH7b18r3bffH8e97x2\nT82aDpfvSdsHIDMAIRJZXfVaD0mamMK+FwiPQtIJ/CQd1DZsxe79mFb8uiivKCuIq+5D00hb07ew\nxWJ+/OdsqzdQerOE6R3TVYsfi2eKZQ9rMEN1F5C/LB/7DK1eRAEIkUlLcLYTYcIu7pW8UWcPwe2j\nVNsKKgvXc4nqmHe1zfspLhYxemi06txKZ0vac/ObZmz1Bs6XzuPEgRO19SA0m3f3dEcuEZokEgUk\nCCkQJuziTDoWNfJFt/3bv3y7ptCMKftm0Kbtei71riDWrh639C244nvTA5u0+3vrCoBwR62pGFAQ\nL0NtVhAFIAgpECbs4gxTjapMdNuvLK8gd0muKix3+M5hp+IvrudSbzEZXciwa9+8/Wuq2jEwNzFX\nUZJhSsjLfOtClhLHiQlIEFIgLAopzmirqMrElmvnntfuqWobuHEg1LTkei6N+Jd0ZkmXvnnY1hV4\n9Q2sTmeyJx/0k6WIOVEAQl1kKSa8FQkTdnGGqUZVJlG2d/EHRTmXOP1LUY4VpgzDnM58npFbk0Nu\ndc55rUAWEBOQEJkkC37UW5i9FbFlH41zJXRU00rcdX2TXtUdBy7+B+/3MvkXiotF7Hxhp7m+NQE9\n+R5M3TaVmXtbZgBCZJIq+JFmDvtm4jp7ims0HNW0kkSobxYix0zXvTBZQOlsqWZ7k9ILmyHpZjxd\nvV0gotDFZs1GFIBgRffQRLUduwq8pCtJZYG0lJyLAG5ns55tJbM/ft8j3583hmuGmbR0ClQXapqF\ne1sUgGDEWHrPEBOum0ZHEXhpJWhrJllVcq08+2pkkZk2fh9AbnUu0gxpcPMgZnbNYOq2Ke2ivfGu\nce2x0r63RQEIRqKU3jNNl6MIvHbJM2TDlsQsTbKqmMJodJGZKX4/TDD7Z1S6PkzdNoWpW6cqKT6y\nem+LE1gwYkuc1ZPvKWevDHHqRRnVx+18zCLGB56QqlOwVWdfjS4yi6N8pDb1s9IrnkIa3DyYyXtb\nFECbUU8UjWkf20NQPFPEueI5jB4atdbPjbK6sxWiRRplZM+IPoqEkeoK0aTr+CZFo4vMrt9+fcOC\nOUxJLr+1jPmj85m8tyUbaBtRT6ZO2z5AbW3VINRN4BU22l7bKXtoXIyT3h7sVZNKg1b9naJUdrNF\nATXi/HbKRdTk31aygXYg9dhxbft4D5At46JnQzXZXtspe2hc9K3Pnj24VX+nOBaZNRqiGrpKGNmd\nSYkCaCPqsePanJLjXeMVQWBNu6uwpe7NuiBpJmkVowmjFX+nLCiuoW1DWHhqoaZGsUcWflsTogDa\nCGPZQFUlKso+AKpW+W4c26iNlw6SdadhFsiC0GonsqC4dLmEgLKJNMtmNHECtxEje0bQ1Vv7k5be\nLBmdwaZ9/HhOrJqMiRqyOtXNGrY0EELrYQwzXeFM/7aiANqIoW1DWHXpqpp2Ww5y0z5BlhaWjKMc\njyxPdQWhHqZ3TOP+nvsxTuO4v+d+TO+Y1m4XJYoqS/muRAG0GcVFfdUmm2nGtI+fvoG+0KLYWZ7q\nCoIOmzCe3jGN2QdnK4EOfJ4x++CsVgm4rmFJMpFiPYQqACK6iIh+QERzRPQcUTmGjYjeTURPE9E8\nEX2DiHKqfZV6f1J9vsF3rPtU+0+J6KNJnVQnU088d5jZxruRjcdWIXci/IVWIkwYnzhwQrufrt11\nDUvYwrVmzw5cZgBvA/gwM28E8H4ANxHRBwH8MYAvM/MggNcB3KG2vwPA68z8XgBfVtuBiN4H4FMA\nrgFwE4D9RBSoMSc0Sj2raW0l9fw3cies1BU6hzBhbEoTYWp38evYIvXSmB2EKgAuc1a97VV/DODD\nAL6p2icA3KJeb1XvoT4fISJS7Y8y89vM/HMAJwHcEMtZCBXqWU2r22f00Ch2c/WN3AkrdYXOISxs\n2pQmwtTugm2GHmcdaFecwkDVSP0EgPcC+FMA/wzgDWY+pzY5DeBK9fpKAC8CADOfI6IlAP2q/fu+\nw/r38X/XdgDbAWBgYCDi6bQGSafdrScsznWfLITcCa2Hyz2f5HOhO3ZYgrbrt1+P2QdrMxH05ntR\nmCzUXZzHtAZk6rYp7T5JhlY7OYGZ+Twzvx/AVSiP2n9Dt5n6b8h0YmwPftcBZh5m5uF169a5dK+l\nyJoTyEaWohWE1sXlnk+6ypzu2GEJ2rbs34Lhzw7XSK7S2VLdfbPNotPIxxQpCoiZ3wDwXQAfBLCG\niLwZxFUAXlKvTwO4GgDU530AFv3tmn06hjSmefXQSopKyDYu93ySz4Xp2C4J2rbs36IVwI30zeQr\nSMPHFmoCIqJ1AJaZ+Q0iygP4HZQdu98B8HEAjwIYA/CE2uVJ9f576vNvMzMT0ZMA/oKIvgTgVwAM\nAvhBzOeTeVol7W6r5ocXsocrUnLVAAAW6UlEQVTLPZ/kc2E7totJs1nPbBorxF18AFcAmFB+gC4A\njzHzt4joJwAeJaI/BPAPAB5W2z8M4BARnUR55P8pAGDm54joMQA/AXAOwF3MfD7e08k+WS0M4VGx\nlRrSQ2RNUQnZx+WeT/K5aPTYzXxmm+1jc4kCepaZr2Xm32Lm32Tm+1X7z5j5BmZ+LzN/gpnfVu3/\nqt6/V33+M9+x9jDzrzLzrzHzseROK7tkOZSyyuxjICuKSmgdXO75JJ+Leo/t+cCWTi3V+AGy8sw2\niiSDazJJTvMajaLQVjby0S43vdBcXO75JJ8L12P7n5/82jxKb5ZwvqSMFF4YC6NS5lHXt6Qj/OJG\nCsK0CXEU9BjvGjfm+rHd9ILQ6uieHx26QjO2Y/Re3IuNYxsxf3S+qUpBCsJ0GCan7eGxw5i6bcrp\nxjPaOi03vdBetNoINi7CZr8eNh+Y6Rn01wkwFU5KC0kG1yYY09GeZ+cwziz7J4TkabXQ3zjXqbgG\nN9h8YMZjBGbV3sAsC+trRAG0CS7O2bDY5aRTPcjCsmzTKmtUgPiVlcvzEzYYihIgEWVgliSiANoE\nbUI3DWEjnaQKlbTa6LITaZU1KkD8ykr3/HT1diHfn3ceDNmSKtpIU8mKD6BNCEY6UBdpsxamFcYp\nC8uyT9bXqPiJW1nVG4UU9JkEHb6DmwczXUpVZgBthH/0/rGJj2XKnt9Ko8tOpZV8QEnkzfGen9FD\nowCAqdumrKZK3ax2bmIOI3tGKjPoLfu3VJlVTZlE01KyogDalKylbk4j0ZUQjazdMzaSUlZRTJWu\nZqgsD8zEBNTGBJeVV1Y2phDiZ0uDK2SHVkn3ndTCsSimynpmtWnk+7EhCqDJNCvOOvg9QVtks+OR\ns3bjC61PEsoqilCv12cS1u9mrsUQBdBEgisFkxLCuu/xL0bxsDlhk7gJW2V0KXQuUYR6lFmt6/PU\nLBnhIQqgibhML6MKXt322lWNhhQPS6eWaqobNfsmFNqbRqqBNXtlchShHiXHkMvzVJgs4PDY4Zro\nvSSj5SQXUBMx5tohYPfK7sj5fEzbuyxp9xP8jkoGxACSEkIwYRPgYfe0LYdOMIQyan6rOM+lXlye\np9BcREpGuCK5gDJI2PQyaqy8aXvq1q8B8LIZBgl+h4RsClGwjXBd7mnTNicOnGjqaNgjblOly/MU\nlosoqWg5CQNtImGha1EFry3/j+57hu80Dwj8x5KQTSEKNiHfSDUw7SDGsn1WcXmebOeUZLScKIAm\nEhZnHVXwGtvVcYPfs2X/lnJbyLFaaUGQkD42Ie9y75q2ydqiqXpxeZ7ya/PafambEjV5iQmoydim\nl1Fj5W3bm77H5TskZFOIgs206XK/mbYx+QBabSAS9jwVJgt4+5dv1+zXnevG1oNbE33uxAmcMeKI\nAoqav2Rw82DTC1YI7UOYo7eVooDSwOQkzvfncc9r99R1TFcnsCiADsH0IE3vmK5ZI9CMSAuhvegE\nQR0n/utlCtGOGvlTtasogM7C9gDawux0C8QACfkUhKSIo/xkGBIG2kGELTSxhdkZF4gFHHsywhOE\neHApP9ksX4cogAyjE7rABWeSFzlQPFOs2deflVBnXwTMYXZAdaSFrAwWhPiwhrESmjrAEgXQRKKM\nonVC9/HbHwcR4XzpPAC94PfjF9Q6bAvG/KMPKeYiCPFhjJpKwewq6wCaRNSSiDqhu7K8UhH+LlAX\nGaeavRf34vrt12tL2A3fOVwl2GVlsCDER5bW2YgCaBJRa5g2Klypm8ArZhOPtzAsuGBs9NAotuzf\nUrWtaZFKqy3IEYQskKXCO6EmICK6GsAjAP4NgBUAB5j5ASJaC+AbADYAeAHAJ5n5dSIiAA8A2Azg\nLQD/hZl/pI41BuB/qkP/ITNPxHs62SXqKNo0TXTFZt+nbsLUbVOY2TWDkT0j1mmnbZFKqy3IEYSs\nkJXU6C4zgHMA/jsz/waADwK4i4jeB+BeADPMPAhgRr0HgE0ABtXfdgAPAoBSGLsBfADADQB2E9Fl\nMZ5LYniVtMa7xq01Qm1ETeegmybGBZ9nJzMUUJ65rCyv1LTnLsll4gYW4ieO+11oDUIVADO/7I3g\nmflNAM8DuBLAVgDeCH4CwC3q9VYAj3CZ7wNYQ0RXAPgogOPMvMjMrwM4DuCmWM8mAaLa7k242P38\nD97MrhlsHNtozIcSFzYzFGCeoRQX7Q5ooTWJ634XWoNIUUBEtAHAtQCeBvAuZn4ZKCsJInqn2uxK\nAC/6djut2kztmSIYqVM6W4olAsYlH0gw6mduYg4bPrQBP//2z6vj9Q1pnWtw3M7mb6i37J3QmkjE\nV2fhrACIaDWAvwawk5l/WTb16zfVtLGlPfg921E2HWFgYMC1e7GgE8Imojhpg0pl9NCocyWgeoV/\nd64b195xbVWOn9LZkjZ01CbMpZh7ZyERX52FUxQQEfWiLPwnmXlKNf9CmXag/r+i2k8DuNq3+1UA\nXrK0V8HMB5h5mJmH161bF+VcGsZlhZ6H6wg4bErtfW502gabHYR/vj+PrQe3Ysv+Ldj5wk6MHhoF\noNYNBNRwmDDPUsSCkDxSC6KzcIkCIgAPA3iemb/k++hJAGMAvqj+P+Fr/xwRPYqyw3dJmYj+FsAf\n+Ry/HwFwXzynEQ+uo5woI+CwKXUUpWOFUDOzADR5R7y5GJeFucuKw6xELAjJo5vxgYDBzYPpdUpI\nDBcT0I0AbgNQIKJnVNv/QFnwP0ZEdwBYAPAJ9dlRlENAT6IcBno7ADDzIhF9AcAP1Xb3M/NiLGcR\nEyZ7d74/j9zqXF15cIxT6lNLxjSwdcH6tAymAvGS7E3QMbRtCAtPLVQnCWRgbmIOAzcOtNVAQPJb\nOSgAZv4/0NvvAaBmGMzl9KJ3GY51EMDBKB1sJiZ796YHNtV9Y9ji+WMT/oCx0lcUm648EAIAzB+d\nrzE1tpsjWPJblZGVwD6SsHeP7Bkxq8+YsJmkXG26Ev4neHSCIzjqyvx2RZLBBYjT3u2NqJ1CNl3Q\nRADl+/PWGYprFI+E/wkenRD62wlKzgVRADFQMZ2cWqpk2Mz351F6sxQpeZsNr4BL1NKNwfUHXl4f\nfyqIoW1D8kAIFToh9LcTlJwLUhGsQVyr+0Slq7cLqy5dheKZYkWpuEbtROqrmlWYUkO7OIvFd9B+\ntPtvGlbHuNWRimB1EvXGryeMs299bSF2XWF2ALE6qkwRQYA+eZzLqE+cae1Ju4f+hq3M7xREAfio\nR5hFNZF4I+rCZKEcbaEYuHGgJg3zvg37YrXLu/TVSyPt+kCI70BoVXRKrt1nPkHaXgEUJgs4dvex\nSgoEm9O0HmEWNW1z6WwJ0zumMTcxF6po4rbLu/SVVxi7V3Y7H1N8B0K70Imz2bYOA53eMY2pW6eq\n8t8UzxTxxGee0IY31iPMoqZtLp4pYvahWacQtLiX5bv0NeqxJXWA0C50Ymho2yqAwmShvJpRw/nS\nee2PahJapopY3uwisgPY4HcPKpq4S8dVrXPQUM+xs1TeThAaoRNns22rAMLi73WmkJE9I+jqrb0k\npTdLNTMG3eyiUYIKKImFaUPbhvQzAQI2jm2MfGxJFie0C6YBIHVR2y6IbFsfQKjWpvII3i+ohrYN\nVfkLPLwZgz93v2l24UxgUZdp1JxENIYpGsjvlI7iDGv3iBGhM9AmwkM5Qq5dfQFtqwBCHZ4MrXPX\nVOnKr1AaXd1blatfLR5bfmsZx+4+VlFAccX+6wib6naiM0wQvHvbVJujHSPb2tYE5OLw9DJy+muf\nGp2XjMo2jdoEc5fksGX/lkofvZuteKZYmX14bUnk5Alz3HaiM0wQgLIS4BX96K4dfQFtqwDCHJ4e\n/uRnT3zmCQxuHjQqDk8Ym5zCrnizDNdFZHEL3zDHbSc6wwTBo5Mi29pWAQBlJbDzhZ0Y/fqoU6jm\n+dJ5PPfYc1bF4QlsnbM4tzqH4c8Ohyod70aKIlDjFL5hjttOegAEIUgnRba1rQ8g6MQMJlIz+QeK\nZ4oVp+Z417jW1l88U0R3rruqrTvXjd996Hdrav3akmpFWUQWt/C1OW47IRmYIJjopDQRbakAdE7M\nuYk53HzgZgBwNqeYBDR1U02Wz2CkEBB+I5miDoI0W/h20gMgCDqaGdmWZvqJtswGaiq1mO/P41zx\nnFXg5vvzuOe1ewCYR/DG/QmR0ih43xFM1Zx0FJAgCNkgqaykHZ0N1GQvd1m0dc0nr6m8No2Evdz/\nQeox00gMvSB0LmknU2xLBRA1QZufYPFrk4AWG7kgCI2SdsRdW0YBmbz4+f7w8E1vQZYNSX8gCEIc\npB1x15YzAJPpBgCmbp0K3b94pliTJgLovFzhgiAkizYQhIDBzYNN+f62dALb2Hv5XidfQLAUYruX\nkBMEIR2md0yXc4sFcoM1IltcncBtaQKysemBTeVEbCEEbXAmZ83hTx+uO01DYbJQk4pCEJJE7rns\nMX90vma9UbNSr3ScAhjaNuSUyC1ogzM5ZXiFjQVmbHgzCn8qirhz/giCH7nnskmajuCOUwAAQlM1\n6CJ6bE4ZU4EZG5JwTWg2cs9lkzQdwaEKgIgOEtErRPRjX9taIjpORPPq/2WqnYjoK0R0koieJaLr\nfPuMqe3niWgsmdNxw1QQBTBH9ISFeEbV1mmHfwmdh9xz2cIzxy2dWqoxSzcrrNwlCuhrAP4EwCO+\ntnsBzDDzF4noXvX+8wA2ARhUfx8A8CCADxDRWgC7AQyjbIA5QURPMvPrcZ2In7BoHV2U0ODmwUqu\nIP+IyL9NbnUOpbMl7Xfm1+bLP6ZjhJBprYIkXBOSQu657FATVMKoFIlq5ur/UAXAzH9PRBsCzVsB\nfEi9ngDwXZQVwFYAj3A5tOj7RLSGiK5Q2x5n5kUAIKLjAG4C8JcNn0EAUzGThacWqpLBjewZqUT5\n6PaZ+vQUsHLhuEunltDV21VJ0eCHugmlN0uV6CKXAiqScE1oNnLPZQdTVb5g9GHS1OsDeBczvwwA\n6v87VfuVAF70bXdatZnaY8dk55x9aNbo/NL+GCuoYWV5BRetuahqQVm+P4+L1lxUkxwuzLYqi8mE\nZiP3XHbIijku7oVgugBLtrTXHoBoO4DtADAwMBC5A8YLaAizGto2FOmiFxeLNQnfxrvGo/VFIXmA\nhGYj91w2yIo5rt4ZwC+UaQfq/yuq/TSAq33bXQXgJUt7Dcx8gJmHmXl43bp1kTsW5QJ6AjrKPrpt\n017OLQhCa5GVojP1KoAnAXiRPGMAnvC1f1pFA30QwJIyEf0tgI8Q0WUqYugjqi12bBE+QTwB7VI/\n2DuO7gfKyo8pCEJrkBVzXKgJiIj+EmUn7uVEdBrlaJ4vAniMiO4AsADgE2rzowA2AzgJ4C0AtwMA\nMy8S0RcA/FBtd7/nEI4bU4TP3MScMd9GcJ/82jxKb5aq7foEDN85rP2BpICKIAhRyYI5ziUK6D8Z\nPqoZ3qron7sMxzkI4GCk3sXEwI1lX0JVvg2uTv0c/DGiJn7Lwo/ZDCQhniC0D22XDM6UtK0n36NN\nAtfssKtWRhLiCUJr0LHJ4ExhoKYMoLIK0h1JJSAI8ZJ2cr62qwcQVaBLpI47WYldFoQsUa9Z1LRo\nFTAvII2btpsBmAR6vj8fGqmTtjbOOhLuKgjVNJJhNQsz6rZTAKaQzE0PbLKGXUmq3HAk3FUQqmlE\niGdhRt12JqCwkEzT1Mr2Q2bNwZlWJI6EuwpZI+2otEaEeBZWA7edAgDqC8nMgja2UbnRvdSxKnir\n2XbDTgl3FbJPFmzojQjxLCTnazsTUL1k2b5dZZ4CUisfJwhZIgs29EbMollYDdyWM4B6yII2NqHN\nVhogKzMVQWgWWZi1N2oWTXtGLQpAYfshs2pn9JOFmYogNJMs2NCB9IV4I4gC8KH7IbNsZ/TIykxF\nEJpJlmftrYL4AELIqp0xrIaxILQ7WbChtzoyAwjBaGe0jMjjRsIvBUFPK5tfsoAogBCM5hcqm4ea\ndfPJjS4IQtyICSiEkT0jxoKWEnopCEIrIwoghKFtQ4bqxRJ6KQhCayMKwIG+9dldJCYIglAvogAc\nkCRogiC0I+IEdkCicARBaEdEATgiUTiCILQbYgISBEHoUEQBCIIgdCiiAARBEDoUUQCCIAgdiigA\nQRCEDoWYDctcMwARvQrgVAOHuBzAazF1JylaoY+A9DNupJ/x0Qp9BJrbz/XMvC5so0wrgEYhollm\nHk67HzZaoY+A9DNupJ/x0Qp9BLLZTzEBCYIgdCiiAARBEDqUdlcAB9LugAOt0EdA+hk30s/4aIU+\nAhnsZ1v7AARBEAQz7T4DEARBEAy0pQIgopuI6KdEdJKI7k27P36I6AUiKhDRM0Q0q9rWEtFxIppX\n/y9LoV8HiegVIvqxr03bLyrzFXV9nyWi61Lu5x8Q0f9V1/QZItrs++w+1c+fEtFHm9THq4noO0T0\nPBE9R0R3q/ZMXU9LP7N2PS8ioh8Q0Zzq57hqfzcRPa2u5zeIKKfaV6n3J9XnG1Ls49eI6Oe+a/l+\n1Z7aM1QFM7fVH4BuAP8M4D0AcgDmALwv7X75+vcCgMsDbXsB3Kte3wvgj1Po128DuA7Aj8P6BWAz\ngGMoF8v8IICnU+7nHwD4fc2271O//yoA71b3RXcT+ngFgOvU60sA/JPqS6aup6WfWbueBGC1et0L\n4Gl1nR4D8CnV/hCAz6rXOwA8pF5/CsA3Uuzj1wB8XLN9as+Q/68dZwA3ADjJzD9j5hKARwFsTblP\nYWwFMKFeTwC4pdkdYOa/B7AYaDb1ayuAR7jM9wGsIaIrUuynia0AHmXmt5n55wBOonx/JAozv8zM\nP1Kv3wTwPIArkbHraemnibSuJzPzWfW2V/0xgA8D+KZqD15P7zp/E8AIEekqezejjyZSe4b8tKMC\nuBLAi773p2G/qZsNA/g7IjpBRNtV27uY+WWg/FACeGdqvavG1K8sXuPPqan0QZ8JLfV+KvPDtSiP\nCDN7PQP9BDJ2PYmom4ieAfAKgOMozz7eYOZzmr5U+qk+XwLQ3+w+MrN3Lfeoa/llIloV7KOm/02j\nHRWATtNnKdTpRma+DsAmAHcR0W+n3aE6yNo1fhDArwJ4P4CXAfxv1Z5qP4loNYC/BrCTmX9p21TT\nlmY/M3c9mfk8M78fwFUozzp+w9KXVPoZ7CMR/SaA+wD8OoB/C2AtgM+n2ccg7agATgO42vf+KgAv\npdSXGpj5JfX/FQCHUb6Zf+FN/9T/V9LrYRWmfmXqGjPzL9TDtwLgz3HBLJFaP4moF2WhOsnMU6o5\nc9dT188sXk8PZn4DwHdRtpuvISKvqqG/L5V+qs/74G42jLOPNykzGzPz2wC+igxdS6A9FcAPAQyq\nCIEcyk6gJ1PuEwCAiN5BRJd4rwF8BMCPUe7fmNpsDMAT6fSwBlO/ngTwaRXJ8EEAS55pIw0CttOP\noXxNgXI/P6WiQt4NYBDAD5rQHwLwMIDnmflLvo8ydT1N/czg9VxHRGvU6zyA30HZX/EdAB9XmwWv\np3edPw7g26w8r03u4z/6FD6h7KPwX8v0n6E0PM9J/6HsYf8nlO2Eu9Luj69f70E5imIOwHNe31C2\nT84AmFf/16bQt79Eebq/jPLo5A5Tv1Cevv6pur4FAMMp9/OQ6sezKD9YV/i236X6+VMAm5rUx3+P\n8nT+WQDPqL/NWbueln5m7Xr+FoB/UP35MYD/pdrfg7ICOgngrwCsUu0Xqfcn1efvSbGP31bX8scA\nvo4LkUKpPUP+P1kJLAiC0KG0owlIEARBcEAUgCAIQociCkAQBKFDEQUgCILQoYgCEARB6FBEAQiC\nIHQoogAEQRA6FFEAgiAIHcr/B/WmKemNrVamAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d4856ad50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 单个特征散点图\n",
    "plt.scatter(range(data_2011.shape[0]), data_2011.cnt.values,color='purple')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Calculates pearson co-efficient for all combinations，通常认为相关系数大于0.5的为强相关\n",
    "data_2011_corr = data_2011.corr().abs()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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m0/tMtFguAPt6dSgy+CPQ6bi7fA235v9ptj6/nw+FP30fQ6Qxz+2lK7i7Yg15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szMihAdHauzt2Lt2kX07z88d77w8yERYZFY2SbfIFlalyQy/P4rcuQeoaHh2Nomt5+NrVWaR92h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\nNiJ+a8aFk7rb9zhWuS8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5bXaX9B8+Jj14nXDiWcRVqwNA1qqF7Hq/r9tRsgylb3yKzJWF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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d44c92110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13, 9))\n",
    "sns.heatmap(data_2011_corr,annot=True)\n",
    "\n",
    "# Mask unimportant features\n",
    "sns.heatmap(data_2011_corr, mask=data_2011_corr < 1, cbar=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.4数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 区别类型与数值型特征\n",
    "X_value_index_list_random = ['temp','atemp','hum','windspeed']\n",
    "X_hot_index_list_random = ['season','weekday','workingday','weathersit']\n",
    "\n",
    "X_value_train = data_2011[X_value_index_list_random]\n",
    "X_hot_train = data_2011[X_hot_index_list_random]\n",
    "X_value_test = data_2012[X_value_index_list_random]\n",
    "X_hot_test = data_2012[X_hot_index_list_random]\n",
    "\n",
    "y_train = data_2011['cnt'].values\n",
    "y_test = data_2012['cnt'].values\n",
    "#用于后续显示权重系数对应的特征\n",
    "columns = data.drop('cnt', axis = 1).columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.5数据预处理/特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lyc/anaconda2/lib/python2.7/site-packages/sklearn/utils/validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "#数值型特征归一化，归一化后多项式拟合，类别型特征热编码\n",
    "from sklearn.preprocessing import StandardScaler,MinMaxScaler,PolynomialFeatures,OneHotEncoder\n",
    "import scipy\n",
    "from scipy.sparse import hstack\n",
    "ss_X = MinMaxScaler()\n",
    "ss_y = StandardScaler()\n",
    "one_hot_encoder = OneHotEncoder()\n",
    "poly = PolynomialFeatures(degree=3)\n",
    "#分别对训练和测试数据的特征以及目标值进行标准化处理\n",
    "X_value_train = ss_X.fit_transform(X_value_train)\n",
    "X_value_test = ss_X.transform(X_value_test)\n",
    "\n",
    "X_value_train = poly.fit_transform(X_value_train)\n",
    "X_value_test = poly.transform(X_value_test)\n",
    "\n",
    "X_hot_train = one_hot_encoder.fit_transform(X_hot_train)\n",
    "X_hot_test = one_hot_encoder.transform(X_hot_test)\n",
    "\n",
    "X_train = scipy.sparse.hstack((X_value_train,X_hot_train))\n",
    "X_test = scipy.sparse.hstack((X_value_test,X_hot_test))\n",
    "#对y做标准化不是必须\n",
    "#对y标准化的好处是不同问题的w差异不太大，同时正则参数的范围也有限\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1, 1))\n",
    "y_test = ss_y.fit_transform(y_test.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.模型选择评估"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1岭回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is 0.8043697000954502\n",
      "The r2 score of RidgeCV on train is 0.8815558269776185\n"
     ]
    }
   ],
   "source": [
    "#岭回归／L2正则\n",
    "#class sklearn.linear_model.RidgeCV(alphas=(0.1, 1.0, 10.0), fit_intercept=True, \n",
    "#                                  normalize=False, scoring=None, cv=None, gcv_mode=None, \n",
    "#                                  store_cv_values=False)\n",
    "from sklearn.linear_model import  RidgeCV\n",
    "\n",
    "#设置超参数（正则参数）范围\n",
    "alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "#n_alphas = 20\n",
    "#alphas = np.logspace(-5,2,n_alphas)\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)  \n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train, y_train)    \n",
    "\n",
    "#预测\n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge)\n",
    "print 'The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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B2qTo+qhhqG9Z7HT3OaEmERE5BUU7D/DwK0V8ZGQvRudmRh2n2apvWUwzs18BzwPlR590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\nInVSWYiISJ1UFiIiUieVhYiI1On/AxLu5w+cPoUoAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d34064210>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 0.01)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "arrays must all be same length",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-17-6ee350c948f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;31m# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mfs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coef_lr\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coef_ridge\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mridge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'coef_lr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/lyc/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    273\u001b[0m                                  dtype=dtype, copy=copy)\n\u001b[1;32m    274\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/lyc/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_init_dict\u001b[0;34m(self, data, index, columns, dtype)\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_arrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_init_ndarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/lyc/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_arrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[1;32m   5494\u001b[0m     \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5495\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5496\u001b[0;31m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5497\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5498\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/lyc/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mextract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m   5542\u001b[0m             \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_lengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5543\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5544\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'arrays must all be same length'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5546\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhave_dicts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: arrays must all be same length"
     ]
    }
   ],
   "source": [
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas),1)) \n",
    "\n",
    "#这是为了标出最佳参数的位置，不是必须\n",
    "#plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.28, 0.29, 0.30])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print ('alpha is:', ridge.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/lyc/anaconda2/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:1094: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n",
      "/home/lyc/anaconda2/lib/python2.7/site-packages/sklearn/linear_model/coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is 0.7222849888907231\n",
      "The r2 score of LassoCV on train is 0.8218152461204571\n"
     ]
    }
   ],
   "source": [
    "#### Lasso／L1正则\n",
    "# class sklearn.linear_model.LassoCV(eps=0.001, n_alphas=100, alphas=None, fit_intercept=True, \n",
    "#                                    normalize=False, precompute=’auto’, max_iter=1000, \n",
    "#                                    tol=0.0001, copy_X=True, cv=None, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=None, selection=’cyclic’)\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "#alphas = [ 0.01, 0.1, 1, 10,100]\n",
    "\n",
    "#生成一个LassoCV实例\n",
    "#lasso = LassoCV(alphas=alphas)  \n",
    "lasso = LassoCV()  \n",
    "\n",
    "#训练（内含CV）\n",
    "lasso.fit(X_train, y_train)  \n",
    "\n",
    "#测试\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "\n",
    "# 评估，使用r2_score评价模型在测试集和训练集上的性能\n",
    "print 'The r2 score of LassoCV on test is', r2_score(y_test, y_test_pred_lasso)\n",
    "print 'The r2 score of LassoCV on train is', r2_score(y_train, y_train_pred_lasso)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UYghMNF8BHCQw0fwx59zWXtq/iuYUPNXS3kFZVSO7qxoorWygrKqRsuoG9lQ1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\nkS4KBRER6aJQEBGRLv8f5vqNVv61GcwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f7d2ccde9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('alpha is:', 0.004733694080671761)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "arrays must all be same length",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-fd2778de257e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;31m# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mfs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"columns\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coef_lr\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coef_ridge\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mridge\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coef_lasso\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlasso\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     12\u001b[0m \u001b[0mfs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msort_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mby\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'coef_lr'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mascending\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/lyc/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    273\u001b[0m                                  dtype=dtype, copy=copy)\n\u001b[1;32m    274\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 275\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    276\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    277\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/lyc/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_init_dict\u001b[0;34m(self, data, index, columns, dtype)\u001b[0m\n\u001b[1;32m    409\u001b[0m             \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 411\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_arrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    412\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    413\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_init_ndarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/lyc/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_arrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[1;32m   5494\u001b[0m     \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5495\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5496\u001b[0;31m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5497\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5498\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/home/lyc/anaconda2/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mextract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m   5542\u001b[0m             \u001b[0mlengths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mraw_lengths\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5543\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5544\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'arrays must all be same length'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5545\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5546\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhave_dicts\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: arrays must all be same length"
     ]
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses) \n",
    "#plt.plot(np.log10(lasso.alphas_)*np.ones(3), [0.3, 0.4, 1.0])\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()    \n",
    "            \n",
    "print ('alpha is:', lasso.alpha_)\n",
    "\n",
    "# 看看各特征的权重系数，系数的绝对值大小可视为该特征的重要性\n",
    "fs = pd.DataFrame({\"columns\":list(columns), \"coef_lr\":list((lr.coef_.T)), \"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lr'],ascending=False)"
   ]
  }
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